The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) was an infrared (IR) limb emission spectrometer on the Envisat platform. Currently, there are four MIPAS ozone data products, including the operational Level-2 ozone product processed at ESA, with the scientific prototype processor being operated at IFAC Florence, and three independent research products developed by the Istituto di Fisica Applicata Nello Carrara (ISAC-CNR)/University of Bologna, Oxford University, and the Karlsruhe Institute of Technology–Institute of Meteorology and Climate Research/Instituto de Astrofísica de Andalucía (KIT–IMK/IAA). Here we present a dataset of ozone vertical profiles obtained by merging ozone retrievals from four independent Level-2 MIPAS processors. We also discuss the advantages and the shortcomings of this merged product. As the four processors retrieve ozone in different parts of the spectra (microwindows), the source measurements can be considered as nearly independent with respect to measurement noise. Hence, the information content of the merged product is greater and the precision is better than those of any parent (source) dataset.

The merging is performed on a profile per profile basis. Parent ozone profiles are weighted based on the corresponding error covariance matrices; the error correlations between different profile levels are taken into account. The intercorrelations between the processors' errors are evaluated statistically and are used in the merging. The height range of the merged product is 20–55 km, and error covariance matrices are provided as diagnostics. Validation of the merged dataset is performed by comparison with ozone profiles from ACE-FTS (Atmospheric Chemistry Experiment–Fourier Transform Spectrometer) and MLS (Microwave Limb Sounder). Even though the merging is not supposed to remove the biases of the parent datasets, around the ozone volume mixing ratio peak the merged product is found to have a smaller (up to 0.1 ppmv) bias with respect to ACE-FTS than any of the parent datasets. The bias with respect to MLS is of the order of 0.15 ppmv at 20–30 km height and up to 0.45 ppmv at larger altitudes. The agreement between the merged data MIPAS dataset with ACE-FTS is better than that with MLS. This is, however, the case for all parent processors as well.

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) was an infrared (IR) limb emission spectrometer onboard the ENVISAT platform. It measured during day and night at 6 to 70 km (up to 170 km in special modes), pole-to-pole, producing more than 1000 profiles per day. Around 30 species, temperature, and cloud composition could be derived from these measurements. In 2002–2004, the instrument operated in full spectral resolution, with a vertical resolution of about 3.5–6 km for the retrieved ozone product; this period of MIPAS operations is referred to as the full resolution (FR) period. Due to a failure of the instrument's mirror slide in 2004, the operations were suspended during almost a year and were resumed in 2005 with reduced spectral, but improved vertical, resolution. The corresponding period, until the loss of communications with the ENVISAT platform in April 2012, is referred to as the reduced resolution (RR) period of MIPAS operations.

MIPAS Level-2 data are operationally processed at ESA, with the scientific
prototype processor at IFAC Florence

all four processors use the same Level-1b spectra provided by ESA, but the Level-2 retrieval algorithms are different;

all four processors use microwindows instead of the full spectrum, but microwindow selection differs; for the rationale behind this approach see

all four processors apply a global fit approach in a sense that the tangent altitudes of a limb scan are processed simultaneously rather than
sequentially

the Bologna processor uses a full 2-D-approach, that is, all measurements in a complete orbit are processed simultaneously; the horizontal variation of the atmospheric state within the orbit plane is considered; the KIT processor accounts for horizontal temperature gradients along the line of sight direction in the ozone retrieval; the other processors consider atmospheric variation in the altitude domain only.

In the frame of ESA's Ozone Climate Change Initiative project, a round robin
evaluation of ozone products from the four MIPAS processors was performed.
Comparison with ground-based instruments revealed that all four processors
reproduce on average the correct ozone distribution in a similar way, with
small differences in bias appearing most clearly in the troposphere. The KIT
bias was shown to be less than

The use of different data points by the four processors gives rise to the expectation for the merged product to have a better precision than the individual contributing datasets. However, it should be treated with caution: this expectation relies on the assumption that the dominating source of uncertainties coming with the data is measurement noise, or any other source of random error which is uncorrelated between the parent datasets. It is, of course, not expected that systematic error components will necessarily average out by the merging operation. The merging was performed on the MIPAS data provided for the round robin exercise only, namely ozone data for the years 2007 and 2008.

Spectral analysis windows used by the four MIPAS processors for ozone retrieval.

The merging is performed on a profile per profile basis. Parent ozone profiles are weighted based on their corresponding random error covariance matrices; the correlations between different altitude levels of the profiles are taken into account. The intercorrelations between the processors' errors are evaluated statistically and are used in the merging. The small sample size (four processors only) is an obstacle to the identification of outliers. It only takes one processor to significantly deviate from the true profile, and the merged product will be worse than any of the other three. Our choice is, however, to always use all four processors' values.

The merged profile is constructed as a weighted mean of the four parent
profiles. For each processor, the errors at different height levels are
correlated because of error propagation patterns typical for limb sounding.
Therefore, the value of the merged profile at each level is a linear
combination of all the levels of all four processors, with weights defined by
corresponding error covariance matrices. This means that the weights depend
on the uncertainties' size: the smaller the error of a processor are, the
larger its contribution to the merged profile is. The merging is performed on
a fixed pressure grid which corresponds approximately to the MIPAS RR nominal
tangent altitude grid. At the upper and lower ends of the profiles, it occurs
frequently that not all four processors provide data. The height range was
hence limited to 62–0.8 hPa (

The random error covariance matrix of the merged profile is given by

As the vertical resolutions of the four processors are very close (see

A retrieved atmospheric profile

We evaluate the intercorrelation of random errors of different processors by examining the statistics of differences between each pair of processors in the following way.

The random errors of processor

By definition, the correlation coefficient

We use hence the following estimator of the correlation between the random
errors

In this formula, the third term in each outer sum is the bias of corresponding processor, by taking it out of the first term we obtain a debiased profile, and the second term in the bracket is the mean around which the variation of debiased profiles is calculated.

Note that the obtained matrices are not symmetric, which is to be expected: there is no reason why the random errors of Bologna at height 20 km and random errors of KIT at height 35 km would be correlated exactly as the random errors of Bologna at height 35 km and random errors of KIT at height 20 km.

Figure

In the previous section we have discussed how the inter-processor
correlations were diagnosed. Now we turn to the inter-level error covariances
of each single processor. These error covariance matrices are needed for each
single processor to construct the processor intercorrelation matrix as given
by Eq. (2). Only two processors, ESA and KIT, provide the covariance matrices
for each profile. For the other two processors, only statistical error
covariance matrices can be evaluated empirically. The error covariance
matrices are taken into the merging for controlling the weight of each
processor in the average. Thus, it is more important to evaluate the
covariance matrices in a consistent way than to have a particularly good
covariance matrix for a subset of profiles. Therefore, we have decided to use
statistical covariance matrices for all four processors. In order to reduce
the correlation due to natural atmospheric variability, we calculate it on
summer profiles in the 20

Correlation of errors of four processors calculated by
Eq. (

Note that this formula can also be obtained from the formula for the
correlation of errors by taking

Figure

Statistical covariance matrices of four parent MIPAS processors. The white areas in Bologna and Oxford plots are for values bigger than 0.3: up to 0.82 for Bologna and up to 0.44 for Oxford processors.

Parent MIPAS profiles and the resulting merged MIPAS profile on
geolocation 33441_20080723T072843Z (0.2

Mean profiles

Mean profiles

Merging of various data products from the same instrument is not necessarily
supposed to remove the bias of the parent datasets. Instead, it is supposed
to ameliorate the precision of the product since the parent processors rely
on different spectral information (different microwindows). At heights where
the precision of the merged product is better than the precision of any of
the parent datasets, the merging is successful. Figure

Figure

We created a 2-year dataset of merged ozone profiles from four independent MIPAS Level-2 processors. The novelty of the product is a mathematically clean way of performing the merging: the weighting of parent profiles is realized by corresponding inverse error covariance matrices, the correlations between different profile levels are considered, and the intercorrelations between processors' errors are evaluated statistically and are used in the merging. In comparison to the individual parent datasets, the merged product has a restricted height range (20–55 km) and only a statistical covariance matrix can be provided. Validation of the merged dataset is performed by comparing with ozone profiles from ACE-FTS and MLS. Comparison with ACE-FTS looks better than with MLS. This is, however, the case for all parent processors as well. Despite the fact that the merging is not supposed to remove the bias, the high bias around the ozone VMR peak known for the parent profiles is reduced in comparison with ACE-FTS (but not with MLS). The overall precision of the merged product is better than that of any of the four processors. This product could therefore be of use in specific studies requiring improved precision of the MIPAS ozone record.

The merged MIPAS data product is available at

The authors declare that they have no conflict of interest.

This work was performed in the frame of European Space Agency (ESA) project Ozone_cci. All four MIPAS teams acknowledge ESA for providing MIPAS L1b data. The ACE mission is supported primarily by the Canadian Space Agency. Work at the Jet Propulsion Laboratory was performed under contract with the National Aeronautics and Space Administration.

We acknowledge support by Deutsche Forschungsgemeinschaft and the Open Access Publishing Fund of Karlsruhe Institute of Technology. The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association. Edited by: M. Weber Reviewed by: two anonymous referees